Method and apparatus for interactive evolutionary optimization of concepts

• US 9,799,041 B2
• Filed: 03/14/2014
• Issued: 10/24/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Nielsen Consumer LLC (Nielsen Holdings plc)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Karty, Kevin D., Lamoureux, Steven H., Lee, Jenkin Espinosa, Flynn, Mark Gerard, Chen, Xinyan
• Primary Examiner(s): Miller, Alan S
• Application Number:
  US14/211,788
• Publication Number:
  US 20140344013A1
• Time in Patent Office: 1,320 Days
• Field of Search: 705 711- 742
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method includes determining a plurality of data points, determining a distance between each data point and each of the other plurality of data points, choosing a first one of the plurality of data points, identifying all of the other plurality of data points within a maximum distance of the chosen data point, repeating steps the previous steps to choose a different one of the plurality of data points until all of the data points have been chosen, identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius and analyzing the one or more clusters with respect to K linkages.

Claims

1. A method comprising:

(i) determining, by executing an instruction with a processor, data points associated with components of a graphical user interface;

(ii) determining, by executing an instruction with the processor, a distance between respective ones of the data points and respective ones of the other data points;

(iii) identifying, by executing an instruction with the processor, a presentation format of respective ones of the components of the graphical user interface;

(iv) one of maintaining or modifying, by executing an instruction with the processor, the determination of the distance between the respective ones of the data points and respective ones of the other data points based on the presentation format of the respective components of the graphical user interface;

(v) choosing, by executing an instruction with the processor, a first one of the data points;

(vi) identifying, by executing an instruction with the processor, all of the other data points within a maximum distance of the chosen data point;

(vii) repeating, by executing an instruction with the processor, (v)-(vi) choosing a different one of the data points until all of the data points have been chosen;

(viii) reducing cluster identification bias by identifying, by executing an instruction with the processor, one or more clusters, respective ones of the one or more clusters having a predefined minimum number, K, of data points within a predefined search radius; and

(ix) preventing identification errors of the one or more clusters caused by outlier data points of the data points by analyzing the one or more clusters with respect to K linkages.

2. The method of claim 1, wherein the analyzing of the one or more clusters includes extending respective ones of the clusters.

3. The method of claim 2, wherein the analyzing of the one or more clusters includes extending respective ones of the clusters to include any data points within a predetermined maximum span of at least m data points forming the cluster, wherein m is less than a total number of all of the data points.

4. The method of claim 2, wherein the analyzing of the one or more clusters includes extending respective ones of the clusters to include any data point that is within a maximum span proportional to a number of data points in the cluster.

5. The method of claim 2, wherein the analyzing of the one or more clusters includes extending respective ones of the clusters to include any data point that is within a predetermined maximum average distance to a nearest n data points in the cluster.

6. The method of claim 1, wherein respective ones of the data points correspond to a click location from the graphical user interface of a product presented on a display.

7. The method of claim 6, further including utilizing information associating a location of respective ones of the data points to an attribute of the graphical user interface to identify the one or more clusters.

8. The method of claim 7, wherein the attribute of the graphical user interface is at least one of a color, a color region, a shape, a text region or a pre-identified region.

9. A tangible computer readable storage device or storage disk comprising instructions which, when executed, cause a processor to, at least:

(i) determine data points associated with components of a graphical user interface;

(ii) determine a distance between respective ones of the data points and respective ones of the other data points;

(iii) identify a presentation format of respective ones of the components of the graphical user interface;

(iv) one of maintain or modify the determination of the distance between the respective ones of the data points and respective ones of the other data points based on the presentation format of the respective components of the graphical user interface;

(v) choose a first one of the data points;

(vi) identify all of the other data points within a maximum distance of the chosen data point;

(vii) repeat (v)-(vi) choosing a different one of the data points until all of the data points have been chosen;

(viii) reduce cluster identification bias by identifying one or more clusters, each of the one or more clusters having a predefined minimum number, K, of data points within a predefined search radius; and

(ix) analyze the one or more clusters with respect to K linkages to prevent identification errors of the one or more clusters caused by outlier data points of the data points.

10. The computer readable storage device or storage disk of claim 9, wherein the instructions, when executed, cause the processor to extend respective ones of the clusters when analyzing the one or more clusters.

11. The computer readable storage device or storage disk of claim 10, wherein the instructions, when executed, cause the processor to extend respective ones of the clusters to include any data points within a predetermined maximum span of at least m data points forming the cluster, wherein m is less than a total number of all of the data points.

12. The computer readable storage device or storage disk of claim 10, wherein the instructions, when executed, cause the processor to extend respective ones of the clusters to include any data point that is within a maximum span proportional to a number of data points in the cluster.

13. The computer readable storage device or storage disk of claim 10, wherein the instructions, when executed, cause the processor to extend respective ones of the clusters to include any data point that is within a predetermined maximum average distance to a nearest n data points in the cluster.

14. The computer readable storage device or storage disk of claim 9, wherein the instructions, when executed, cause the processor to identify respective ones of the data points corresponding to a click location from the graphical user interface of a product presented on a display.

15. The computer readable storage device or storage disk of claim 14, wherein the instructions, when executed, cause the processor to utilize information associating a location of respective ones of the data points to an attribute of the graphical user interface to identify the one or more clusters.

16. The computer readable storage device or storage disk of claim 15, wherein the instructions, when executed, cause the processor to select the attribute of the graphical user interface to be at least one of a color, a color region, a text region or a pre-identified region.